A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.

logs, apps used), and behavioral (e.g., sleep duration) signals can be captured via sensors 18 .Compared to questionnaires only capturing a certain moment in time, passive sensing allows an objective and continuous flow of data 14,17 .
Several viewpoint articles have delineated promises and challenges associated with the application of passive sensing within the field of suicide prevention 14,[19][20][21] .Nonetheless, a systematic review of the current state of research remains absent.Against this backdrop, we aimed to systematically synthesize the current evidence on passive sensing for the prediction of STB.This review encompassed the exploration of three core facets: (1) the potential of passive sensing data to enhance STB prediction, (2) the discriminative efficacy of various sensor types, and (3) an examination of the statistical analysis methodologies documented in the literature.Additionally, we synthesized findings pertaining to the feasibility of employing passive sensing for the assessment of STB.

Methods
This report follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 22 (see Supplementary Table 1).All procedures were pre-registered (https://osf.io/hzxua)and reported in a study protocol 23 .Due to the infancy of the field, we decided to broaden the scope of the review by a) additionally including feasibility papers and study protocols, and by b) additionally including prediction papers with non-clinical samples, regardless of the presence of STB.

Eligibility criteria
We included studies with participants with and without STB at baseline, without restrictions on age, gender, or current treatment status.Studies had to address the use of passive data through smartphones or wearables in the context of STB.We included all passive sensor modalities.We included studies regardless of whether participants received treatment.Studies were eligible if they reported a quantitative measure of STB as an outcome; we excluded studies restricted to non-suicidal self-injurious behavior.Studies were eligible if they reported results either on the association between passive data and STB or on the feasibility of passive sensing.Study protocols were additionally included to provide a perspective on the research currently being processed.We included peer-reviewed articles without any restrictions on language or publication date.

Search strategy
The following databases were searched from inception to June 24, 2024: MEDLINE, PubMed, Embase, PsycINFO, and Web of Science.The search string is displayed in the study protocol 23 .We validated the search string against seven hand-searched relevant articles; the search string reached a coverage rate of 100%.The relevant articles were screened using the online tool Covidence.In the first step, two independent researchers screened all titles and abstracts against the eligibility criteria.After obtaining all relevant full texts, a second screening of the articles was conducted.Conflicts were resolved in discussion with a third reviewer (LS) where necessary.

Data extraction
All relevant information was extracted from the final set of studies and double-checked by an independent researcher, including the following variables: Study identification items, study design, sociodemographic and clinical sample characteristics, data collection device, type and collection frequency of passive data, sensors used, assessment of STB, assessment length, and analysis methods.Any findings about an association between the passive data and STB or the predictive value of the passive data for STB were extracted.Descriptive results on feasibility of passive sensing were extracted as secondary outcomes.

Risk of bias assessment
Two independent researchers assessed risk of bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) tool 24 .The PROBAST tool is specifically designed to assess potential sources of bias in studies for the development or evaluation of prediction models.It covers the following domains: participants, predictors, outcome, and analysis.Conflicts were resolved in discussion with a third researcher.

Data analysis
The results were described narratively.In the field of prediction modeling, there are a variety of performance measures, which complicates the reporting and interpretation of the findings.Therefore, we selected and reported performance measures of prediction models in line with a decision tree developed by WVB and MC 25 that provides a hierarchy of the most meaningful performance measures, along with a guideline for the interpretation of the predictive value (i.e., very good, good, acceptable, or poor).These were reported in the section "Main Findings of Prediction Studies".Where deemed helpful for the understanding of the findings, additional measures were reported.A meta-analytic synthesis of predictive validity was not possible due to the heterogeneity in the identified studies.

Characteristics of prediction studies
The study characteristics are displayed in Table 1.Sample sizes ranged from 25 32 to 2881 participants 30 .More than half of the studies were based on clinical populations, i.e., participants were in inpatient or outpatient treatment 26,29,[32][33][34][35][36] .All non-clinical studies had larger sample sizes than clinical studies, at least 281 27 .One study recruited adolescents 33 , whereas the other studies were in adult populations.
Studies differed in how they assessed STB.Several studies assessed suicidal ideation using single items of validated scales, (e.g., item 9 from the PHQ-9) 26,28,30,31 .Furthermore, the Columbia-Suicide Severity Rating Scale (C-SSRS) was frequently used 26,29,33 , and one study used the Suicide Behaviors Questionnaire (SBQ) 27 .The studies using the C-SSRS and the SBQ used a composite score, which did not differentiate between suicidal thoughts and behaviors.One study assessed suicide attempts and emergency department visits via electronic health records 34 .Studies divided the outcome into the presence or absence of STB (8/11 studies) [28][29][30][31][32][33][34][35] and/or evaluated STB on a severity level (5/11 studies) 26,27,32,33,36 .Eight studies assessed STB between one and three times in total, and two studies used EMA items to collect momentary data on suicidal ideation several times a day 32,35 .

Main findings of prediction studies
Seven studies developed predictive models based on multiple predictors [28][29][30][31][32]34,35 ; details on the study designs are displayed in Table 1. In anEMA study with suicidal inpatients (n = 25), Kleiman and colleagues 32 investigated the electrodermal activity (i.e., autonomic arousal) until 28 days after discharge.While physiological data did predict suicidal ideation both concurrently (according to a performance score on the percentage of investigated performance indices that speak for best fit: 0.00-1.14%;conditional R 2 = 0.76-0.92;moderate to strong relationship) and prospectively (performance score: 0.00-40.80%;conditional R 2 = 0.78-0.94;moderate to strong relationship), multilevel prediction models performed poorest when only physiological data was included (i.e., poorer than models with only selfreported affective states, with performance score: 29.99-99.95%;conditional R 2 = 0.80-0.96; stong relationship).When physiological data was added to self-reported affect, the fit improved for the outcome severity of suicidal ideation in seven out of eight models (performance score: 59.40-100.0%;conditional R 2 = 0.80-0.83;strong relationship vs.only self-report performance score: 54.33-86.05%;conditional R 2 = 0.80-0.81;strong relationship) and worsened for the presence of suicidal ideation in seven out of eight models (performance score: 27.90-83.02%;conditional R 2 = 0.92-0.96;strong relationship vs. oly self-report performance score: 29.99-99.95%;conditional R 2 = 0.92-0.96;strong relationship).In an EMA study with suicidal emergency department patients (n = 102), Czyz and collegues 35 assessed sleep duration, steps, and resting heart rate.While the passive sensing data predicted next-day suicidal ideation, the model performance was poor (mean AUC = 0.56).When combined with EMA data, the model performance was good (mean AUC = 0.84); however, passive sensing did not improve the prediction compared to EMA only (mean AUC = 0.84; good performance).
No other studies assessed momentary STB.Haines-Delmont and colleagues assessed actively collected (i.e., sociodemographic variables, mood assessments, journaling) and passively collected data (sleep, steps, smartphone usage) in a sample of adults admitted to acute mental health wards 29 .The optimal prediction model for STB was the k-nearest-neighbors algorithm; the performance was poor (AUC = 0.65).In a prospective study, Barrigon and collegues 34 passively monitored adult outpatients (n = 225) with a history of suicidal ideation or attempt for six months via their smartphones.Individual daily activity profiles were created based on distance traveled, steps, time at home, and app usage, to predict risk events (suicide attempts or emergency department visits requiring psychiatric assessment) within a 1-week window.The machine learning model based on these profiles predicted short-term risk with a good model performance (AUC = 0.78).The study by Horwitz and colleagues 30,31 assessed sleep, physical activity, heart rate, and mood using a Fitbit and a mobile app in first-year medical residents during the first three months of internship.In participants from an earlier wave (academic years 2018-2020, n = 2881), the three-step hierarchical logistic regression indicated that the passive data did not have incremental predictive validity for the prediction of suicidal ideation over baseline demographic and clinical variables (step one AUC 0.735, acceptable performance; vs. step two 0.739, acceptable performance; BIC step one 1125.0vs. step two 1155.2) 30 .In a later cohort (academic year 2020-2021, n = 2459), a machine learning model 31 that only used daily selfreported mood performed better (i.e., a sensitivity of 0.70 and a specificity of  0.73, AUC = 0.74; acceptable performance) than the model that additionally incorporated passive data (i.e., sensitivity of 0.67 and a specificity of 0.69, AUC = 0.70; acceptable performance).Dogrucu and colleagues 28 assessed active data (i.e., a brief voice sample) combined with passive social media account data and smartphone usage data (i.e., GPS, browser history, call logs) that was collected retrospectively from smartphones in a non-clinical adult sample (n = 335).The machine learning algorithm predicted presence vs. absence of suicidal ideation with a good performance of F1 = 0.85 using active and passive data; no alternative models were reported.Four studies 26,27,33,36 investigated associations of single passive data modalities with STB.In a sample of adult outpatients diagnosed with bipolar disorder (n = 76), Bertrand and colleagues 26 assessed sleep characteristics via wrist actigraphy.Using three different measurements of STB and no correction for multiple testing, they found associations of several sleep variables with one of the STB measures each, inconsistent across instruments and timepoints.Sheridan and colleagues 33 investigated the heart rate variability using a wrist device in acutely suicidal adolescents (n = 51).They found an increase in the high-frequency component (i.e., higher parasympathetic activity) in patients that had at least a 25% decrease in STB within the 7-day study period.Salvatore and collegues 36 assessed the circadian activity using a wrist device in adult outpatients with a current major depressive episode and either a major depression or bipolar disorder.They found no association between the self-rated wish to die and circadian activity.This included the amplitude (β = −0.19,p > 0.05), daytime activity (β = −0.19,p > 0.05), mesor (β = −0.19,p > 0.05), and nighttime activity (β = −0.11,p > 0.05).However, they found a positive correlation between these activity indices and the self-rated wish to live and the wish to die.Coyne and colleagues 27 assessed smartphone app usage in a non-clinical cohort of adolescents (n = 281).They found that in girls, a higher use of entertainment apps was associated with an increased risk for STB (composite score; β = 0.23-0.25,p < 0.001); in boys, the use of reading apps (e.g., Reddit) was associated with an increased higher risk for STB (β = 0.28-0.29,p < 0.001).

Risk of bias in prediction studies
Risk of bias in prediction model studies was assessed using the PROBAST tool.The ratings per domain are displayed in Table 2 (see Supplementary Table 2 for detailed ratings).Most studies (7/11) had a high risk of bias in the domain of participants, mostly because the eligibility criteria and reasons for exclusion were unclear.Across all studies, the bias potential was low in the domain of predictors.Furthermore, the bias potential regarding the outcome was high in most trials (9/11).STB were often assessed using single items, drastically limiting the reliability and validity of the assessment 53 .An additional potential for bias was introduced by choosing arbitrary cut-offs to determine high or low suicide risk, or insufficient information on how this was calculated.One study assessed change by 25% in C-SSRS scores 33 , which does not account for measurement error like in the Reliable Change Index 54 .Risk of bias was mostly high in the domain of analysis (8/11 studies).Most studies were either not pre-registered or had vague pre-registration, failing to provide clear information on how predictors were assessed in a timeresolved manner, selected, and analyzed.Studies rarely reported on how they dealt with missing data.There was often imbalance in the sample regarding the proportion of participants with the outcome; frequently, it was unclear how imbalance was managed.Another critical issue in the analysis domain was the insufficient validation of prediction models.Only three studies used a train-test split approach or cross validation of the model 29,31,35 .This failure to test on out-of-sample observations exposes to the risk of overfitting and does not allow conclusions to be drawn on the generalizability of the prediction results.

Main findings of feasibility studies
Several studies investigated the user experience or satisfaction with passive sensing technology.Adolescent inpatients with acute suicidality rated discomfort in wearing a wrist device (Empatica E4), scoring below 5/10 in most items of the Wearable Computer Comfort Rating Scale 43 .Qualitative findings revealed complaints about the device (e.g., too clunky), neutral responses, and positive views (e.g., liking to help research) 43 .In a sample of adolescents who wore a wrist device (Actiwatch) after discharge from acute psychiatric care for suicide risk, 71% found wearing the device comfortable; clinicians also reported that the study participation (that additionally involved EMA) had a neutral to positive impact on the patients 42 .In a study by Jiang and collegues 40 with young adult suicidal patients, 92.6% found the Fitbit comfortable, 18.1% reported that it interfered with daily activities, and 85.1% stated that they would wear it outside the research context.The satisfaction with wearing it was rated 3.89 (SD = 1.19) on a 0-5 scale.The most frequent barriers were water-related activities (45.7%), forgetting to wear it (39.4%),and other activities (20.2%).Another study found that the majority of adult inpatients provided positive qualitative feedback on the use of a Fitbit 41 .A study that investigated a passive sensing smartphone application in adult outpatients found a mean satisfaction with the app of 7/10 44 .In addition, the most frequently reported positive aspect of the app was that it helped control physical activity; the most frequent negative aspect was technical problems 44 .
Regarding adherence to passive sensing technology, two studies on adolescents with STB found that they wore a wrist device on more than 75% of days 42,43 .A total of 7 out of 50 participants stopped wearing the device before discharge 43 .The study by Jiang and collegues with young adult suicidal patients recruited via emergency departments 40 found that in an 8-week monitoring phase, a wrist device was worn on 65.9% of days, and in  53.6% of minutes.Adherence decreased during the monitoring period, with 37.3% of participants stopping to wear the device before the end of the study.In a study with adult bipolar patients 39 , a smart ring was worn on 79.5% of days; the likelihood for perfect adherence was higher in patients with a history of suicide attempt.A study with an adult outpatient sample 44 and a study with adult patients and university students 45 investigated how many participants kept a passive sensing smartphone app installed and did not withdraw from the study.They found retention rates of >65% across all groups after two months 45 , 74% after three months, and 47% after 6 months 44 .
In a sample with tertiary students (n = 302) 38 , only 33 (10.9%) opted to share data for depression and suicidal ideation screening via a smartphone app as opposed to a web app and were asked to share optional passive data via the app.Of these, 21 (63.6%)shared GPS, 11 (33.3%)calendar/contacts, and 10 (30.3%) call/text logs.Of 171 students who reached the Twitter page, 47 (27.5%) had an account, which 16/47 (34.0%) were willing to share.In a sample recruited online via Mechanical Turk (n = 70) 37 , participants were more willing to share active compared to passive smartphone data.Active data included questionnaires for depression and anxiety screening, demographic data, scripted and unscripted audio, and sharing rates ranged from 78.6% to 100% for these modalities.In contrast, a total of 31 (44.3%)shared text messages, 28 (40.0%)calendar, 25 (35.7%)call logs, 14 (20.0%)GPS, 14 (20.0%)Twitter name, 0 (0%) Instagram.In an online survey study by Dogrucu and colleagues 28 , where the sample was intended to be representative of the general population, participants reported a higher willingness to share active data (e.g., a brief voice sample) compared with passive data (e.g., browser history).A study with adult inpatients found that beyond compulsory passive data collection using Fitbit, 80% of participants did not provide optional access to their Facebook data 41 ; the majority of reported reasons for opting out were not having an account (56%) or currently not using it (21%).

Upcoming studies
In total, seven study protocols planning to investigate the predictive validity of passive sensing for STB were identified (see Supplementary Table 3 for a detailed overview) [46][47][48][49][50][51][52] .All planned studies have a longitudinal prospective design.They will all investigate behavioral signals, while two will also include physiological signals 48,49 and three will examine social signals 47,48,51 .Most studies [47][48][49][50][51][52] plan to investigate sleep characteristics; four studies will additionally assess other variables such as physical activity [47][48][49]51 . Sel and colleagues provided detailed information about the sensors used and the collection frequency of data for each sensor in their protocol 51 .Brown and colleagues 49 reported that data will be available at a one-minute resolution (i.e., physical activity, sleep, heart rate) and Victor and colleagues 52 reported a sampling frequency of approximately 30hz (i.e., accelerometers to derive sleep data).The other protocols did not provide detailed information about the frequency and method of aggregating the data.The majority of study protocols designs reported an EMA assessment of STB [47][48][49][50][51][52] .Data collection is planned between 14 days 50 and 60 months 47 . On study reported nonclinical recruitment at schools 46 ; all other studies plan to recruit participants from clinical settings. For studies will only include patients with suicidal ideation or attempts 47,49,51,52 .

Discussion
This is the first systematic review to address the predictive validity and feasibility of passive sensing for the prediction of STB.Our results indicate that the current evidence base does not permit conclusions about the value of passive sensing modalities for predicting STB.Prediction studies in this nascent field of research are highly heterogeneous in the selection of predictors, samples, modeling strategies, and outcomes.Furthermore, due to the explorative nature of the research so far, there are multiple sources of potential bias and shortcomings in the reporting of methods and results.Thus, the predictive value of passive sensing data remains inconclusive.Nevertheless, preliminary results on models incorporating self-reported outcomes with passive sensing data are broadly in line with machine learning approaches using other data modalities 55 .Early feasibility studies suggest that passive sensing might be feasible in clinical populations, in terms of user experience and adherence in terms of wearing devices or keeping mobile apps.Moving forward, we propose several avenues for future research to further explore the potential of passive sensing in the prediction of STB.
The prediction studies identified in this review indicated that passive sensing may have no incremental value over active data in predicting STB.Out of four studies investigating the incremental predictive value of passive data over self-reported data, only one study-a study that performed shortterm prediction of suicidal ideation based on EMAfound an incremental value of passive sensing (i.e., electrodermal activity) in the prediction of suicidal ideation severity in suicidal inpatients 32 .However, physiological data worsened the model fit for the occurrence of suicidal ideation 32 .Another study predicting the short-term presence or absence of suicidal ideation in suicidal patients did not find an incremental value of passive data over EMA 35 ; two other studies did not identify an incremental value of physiological data over daily mood assessments in the prediction of STB at three-month follow-up in medical interns 30,31 .Algorithms based only on passive modalities did predict STB 32,34,35 , usually with a lower model performance compared with active data only 32,35 .Notably, in one study, events including suicide attempts and emergency department visits were predicted in the short-term with a good model performance using activity profiles from passive smartphone sensing 34 .Other studies found correlations of post-test STB with specific app usage in adolescents 27 , sleep characteristics 26 , and the high-frequency component in heart rate variability 33 .There was a high risk of bias overall, with severe shortcomings in the study design and transparency in reporting.
Nevertheless, preliminary feasibility studies suggest that passive sensing for STB is feasible in high-risk populations.Patients with STB and/or mental disorders reported moderate to high levels of comfort while wearing wrist devices 40,42,43 and using a sensing app 44 , with sufficient adherence to these devices, including a smart ring 39,40,42,43 .However, participants from the general population seem to be more willing to share active data compared to passive data 28,37,41 .Potentially, individuals might be reluctant to share passive data under some circumstances such as a non-clinical setting or the possibility to opt out of specific passive data.
The findings of this review hold important implications for future research.First and foremost, studies should address general methodological limitations by adhering to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist 56 and by avoiding potential sources of bias according to the PROBAST tool 24 .Studies should rigorously report the following aspects both in pre-registrations and research reports: eligibility criteria and reasons for exclusion from analyses, missing data handling, details regarding the selection of predictors and feature extraction algorithms, pre-processing steps of passive sensing data, a detailed list of sensors, the sampling rate and method for each sensor, as well as the model performance.Concerning the sampling strategy, studies should select participants with the aim of achieving a balanced sample containing a substantial proportion of participants or time points with and without the outcome of STB, which is necessary for model development and comparison to a control/naive predictor 24 .Authors should report how they dealt with potential class imbalance in classification tasks.In addition, studies should avoid choosing arbitrary cut-offs for differentiating participants into high-and low-risk 24 .Finally, it is crucial to evaluate models using external data 24 .
Second, it is crucial to prioritize the valid and reliable assessment of STB.Single-item measures, as frequently used in the reviewed studies, may not have sufficient psychometric properties 53 .In addition, suicidal ideation and suicidal behaviors should be differentiated (e.g., the subscales of the CSSR-S) instead of using a composite score including both of them, as they are likely to have different risk trajectories 57 .Furthermore, most studies have assessed STB at single time points.Given the high fluctuations in STB 8 , it might be fruitful to additionally investigate the short-term prediction by assessing these outcomes using EMA 5 .
Third, research on passive sensing in the context of STB should consider different application settings and target groups.Target groups identified in this review included acutely suicidal patients 32,33 , patients with a history of STB 34 , groups at risk of developing STB (i.e., people in mental health treatment 26,29,36 or medical interns 30,31 ), as well as the general population 27,28 .Each application area presents unique challenges.The shortterm prediction of STB within individuals with acute STB seems promising both clinically and methodologically.Clinically, intensive monitoring is desirable in this vulnerable target group, while methodologically, a high frequency of time points with STB is necessary for developing predictive algorithms.Developing algorithms for predicting suicide attempts might however prove challenging because of the low rates of occurrence of this outcome and the low signal-to-noise ratio of passive sensing data.Transferlearning on models initially developed for a different but closely related source domain 58 might be one of the possible workarounds to this methodological hurdle.Another application area of passive sensing could be universal screening for STB; again, studies should address sample imbalance in samples from the general population.Another aspect to consider in the application setting is the use of passive sensing variables as the sole predictors versus combining them with EMA data for predicting STB.Passive data alone might be especially useful for longer monitoring periods due to the low participant effort, but it remains unclear whether passive data alone has sufficient predictive value.Combining EMA and passive sensing holds promise for the longitudinal prediction of STB, making use of the advantages of both assessment strategies, including additional data such as nonresponse 59 or response latency to questionnaire items 60 .Regarding the choice of devices for assessing predictors, it is important to consider their real-world applicability in different settings.Participants from the general population seem more willing to share actively assessed data compared to passive smartphone usage data 28,37,41 ; however, this might not generalize to indicated or selective prevention, such as risk monitoring in individuals with acute suicidal ideation or a recent suicide attempt.Furthermore, the applicability of passive monitoring may be influenced by the availability of devices (i.e., higher availability of smartphones compared with wearables), requiring a balance between data quality and scalability.It is crucial to further consider the applicability of passive monitoring for STB beyond research settings, particularly with regards to how an individual's awareness of STB monitoring via passive devices could influence behaviors in ways that would affect the reliability of risk detection in real-world settings.
Several limitations of this review should be considered.First, in line with the protocol, the search string aimed to identify prediction studies and might have missed some relevant feasibility studies.Second, the heterogeneity of reviewed papers did not allow a meta-analytic pooling of results.Third, the current results on social signals are limited to quantitative features, such as number of calls or social media application usage.However, contents of social media posts might be highly relevant for the prediction of STB, as indicated in language processing models [61][62][63] .
In conclusion, the field of passive sensing of STB is currently in its infancy, with the first studies published in recent years.The selection of predictors has been largely explorative, and existing studieswhilst pioneering in their own right -have major methodological shortcomings consistent with first-in-field research.The identified study protocols indicate that studies conducted in the following years may be designed with further rigor to hopefully reveal whether passive data is suitable to predict STB.Future studies should rigorously follow reporting guidelines and pre-register their procedures.Alongside this, the clinical implications of applying such approaches in real-world settings should be considered, to develop tools with maximal utility for helping to detect and ultimately prevent STB among vulnerable populations.

Table 1 |
Characteristics of prediction studies C-SSRS Columbia-Suicide Severity Rating Scale, EMA ecological momentary assessment, GPS Global Positioning System, MADRS Montgomery Åsberg Depression Rating Scale, PHQ-9 Patient Health Questionnaire, QIDS-SR Quick Inventory of Depressive Symptomatology, SBQ-R Revised Suicidal Behavior Questionnaire, STB suicidal thoughts and behaviors, Wi-Fi wireless fidelity.

Table 2 |
Risk of bias of prediction studies PROBAST Prediction model Risk OF Bias ASsessment Tool, ROB risk of bias.+ indicates low ROB; -indicates high ROB.

Table 3 |
Characteristics of feasibility studies EDA electrodermal activity, GPS Global Positioning System, SI suicidal ideation.